The declining cost and rising penetration of solar energy is poised to fundamentally impact grid operations, as utilities must continuously offset, potentially rapid and increasingly large, power fluctuations from highly distributed and "uncontrollable" solar sites to maintain the instantaneous balance between electricity's supply and demand. Prior work proposes to address the problem by designing various policies that actively control solar power to optimize grid operations. However, these policies implicitly assume the presence of "smart" solar modules capable of regulating solar output based on various algorithms. Unfortunately, implementing such algorithms is currently not possible, as smart inverters embed only a small number of operating modes and are not programmable.
To address the problem, this paper presents the design and implementation of a software-defined solar module, called Helios. Helios exposes a high-level programmatic interface to a DC-DC power optimizer, which enables software to remotely control a solar module's power output in real time between zero and its current maximum, as dictated by the Sun's position and weather. Unlike current smart inverters, Helios focuses on enabling direct programmatic control of real solar power capable of implementing a wide range of control policies, rather than a few highly-specific operating modes. We evaluate Helios' performance, including its latency, energy usage, and flexibility. For the latter, we implement and evaluate a wide range of solar control algorithms both in the lab, using a solar emulator and programmable load, and outdoors.
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This content will become publicly available on June 28, 2024
Distributed rate control of smart solar arrays with batteries
Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback of increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion of proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. We also study the benefits of using energy storage when we rate control solar. To do so, we present a decentralized algorithm to charge and discharge batteries for each smart solar. Our evaluation on a city-scale dataset shows that our approach enables 2.6× more solar penetration while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30× faster convergence. Finally, we demonstrate energy storage can help netmeter more solar energy while ensuring fairness and grid constraints are met.
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- NSF-PAR ID:
- 10441181
- Date Published:
- Journal Name:
- Frontiers in the Internet of Things
- Volume:
- 2
- ISSN:
- 2813-3110
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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